Rank of J is deficient

Identification/Sensitivity
1

Hi everyone
when running identification command I got the following output

==== Identification analysis ====

Testing prior mean
Evaluating simulated moment uncertainty … please wait
Doing 231 replicas of length 300 periods.
Simulated moment uncertainty … done!

All parameters are identified in the model (rank of H).

WARNING !!!
The rank of J (moments) is deficient!

[Eta_Gl,Eta_Gh] are PAIRWISE collinear (with tol = 1.e-10) !
[Eta_tk,Eta_I] are PAIRWISE collinear (with tol = 1.e-10) !
[rho_Gl,rho_Gh] are PAIRWISE collinear (with tol = 1.e-10) !
[alpha_Gh,alpha_Gl] are PAIRWISE collinear (with tol = 1.e-10) !

rho_I is collinear w.r.t. all other params!
rho_tk is collinear w.r.t. all other params!

==== Identification analysis completed ====

97.1% of the prior support gives unique saddle-path solution.
0.0% of the prior support gives explosive dynamics.
2.9% of the prior support gives indeterminacy.
Smirnov statistics in driving acceptable behaviour
rho_Gh d-stat = 0.350 p-value = 0.000
rho_Gl d-stat = 0.370 p-value = 0.000
alpha d-stat = 0.602 p-value = 0.000
psi_h d-stat = 0.283 p-value = 0.000

Smirnov statistics in driving indeterminacy
rho_Gh d-stat = 0.340 p-value = 0.000
rho_Gl d-stat = 0.360 p-value = 0.000
alpha d-stat = 0.584 p-value = 0.000
psi_h d-stat = 0.275 p-value = 0.000

Starting bivariate analysis:

Correlation analysis for prior_stable
No correlation term with pvalue <0.001 found for prior_stable

Correlation analysis for prior_unacceptable
[rho_Gh,rho_Gl]: corrcoef = -0.459
[sigma_c,ksi_w]: corrcoef = -0.421

Correlation analysis for prior_indeterm
[rho_Gh,rho_Gl]: corrcoef = -0.459
[sigma_c,ksi_w]: corrcoef = -0.421

What does it mean?
What would you suggest?
Thank you

2

Without the mod-file it is hard to tell. The general explanations can be found at ifk-cfs.de/fileadmin/downloads/events/conferences/monfispol2011/RATTO_IdentifFinal.pdf

3

Here is the mode file

Please have a look at it

Thank you again

Sincerely yours
data_model.xls (47 KB)
rica.mod (12.6 KB)

4

It means that those parameters cannot be identified, because the observables do not provide sufficient information.
For example, the two processes

Gh = rho_Gh*Gh(-1) + (1-rho_Gh)*Y + Eta_Gh; // high government spending dynamics Gl = rho_Gl*Gl(-1) + (1-rho_Gl)*Y + Eta_Gl; // low government spending dynamics
are completely indistinguishable when not observing Gh or Gl. You cannot estimate them separately.

5

Thank you Sir

I will remove these two equation and replace them by only this G=rho_g*G(-1)+Eta_G. maybe this will resolve the issue

What do you think Sir Jpfeifer?

best regards

6

That will help for these two equations. There may be another issues that may require additional steps. After replacing the equations, run identification again.

1 Like
7

Dear jPfeifer

Thank you for your help
As you suggest there is really an issue in these two equations then I removed them!
Now Can you have a look on the following mode file. Rank of J is still deficient! I don’t know Why
May you interpret the graph resulting from the identification and the sensitivity analysis! What really going on with this model?

thank in advance!
SA.mod (12.7 KB)

8

I have a similar question.For a mixed monetary policy,we say using both quantitative and price-based monetary policy tools,the rule follows ,for example:

psi*(log(r)-log(rss ))+(1-psi)*(log(m(-1))-log(m))=psi*(rhor*(log(r(-1))-log(rss) )+(1-rhor)*(rhopi*(log(pi)-log(piss))+rhoy*(log(y)-log(y(-1)))))+(1-psi)*(rhom*(log(z(-1))-log(z))+(1-rhom)* (rhopim*(log(pi)-log(piss))+rhoym*(log(y)-log(y(-1)))))+etar

MY question is :Couldn’t all the parameters in above monetary policy rule be estimated in Bayesian estimation?Cause my mode file gets the following
estimate.mod (17.6 KB)
obs.xlsx (15.0 KB)
message:
!!!WARNING!!!
The rank of Tau (Jacobian of steady state and reduced-form solution matrices) is deficient!

[rhopim,rhopi] are PAIRWISE collinear!
[rhoym,rhoy] are PAIRWISE collinear!

SPECTRUM (QU AND TKACHENKO, 2012):
!!!WARNING!!!
The rank of Gbar (Jacobian of mean and spectrum) is deficient!

[rhopim,rhopi] are PAIRWISE collinear!
[rhoym,rhoy] are PAIRWISE collinear!

MOMENTS (ISKREV, 2010):
!!!WARNING!!!
The rank of J (Jacobian of first two moments) is deficient!

[rhopim,rhopi] are PAIRWISE collinear!
[rhoym,rhoy] are PAIRWISE collinear!

Collinearity patterns with 1 parameter(s)
Parameter [ Expl. params ] cosn
rhopi [ rhopim ] 1.0000000
rhoy [ rhoym ] 1.0000000
rhopim [ rhopi ] 1.0000000
rhoym [ rhoy ] 1.0000000
SE_etar [ psi rhor ] 1.0000000
rhor [ SE_etar psi ] 1.0000000
rhopi [ rhopim – ] 1.0000000
rhoy [ rhoym – ] 1.0000000
rhopim [ rhopi – ] 1.0000000
rhoym [ rhoy – ] 1.0000000

9

As the message indicates, you cannot estimate those parameters individually. Rather, they are only jointly identified.

10

Got it.Thank you.